Purpose

The business intelligence (BI) literature is in a flux, yet the knowledge about its varying theoretical roots remains elusive. This state of affairs draws from two different scientific communities (informatics and business) that have generated multiple research streams, which duplicate research, neglect each other’s contributions and overlook important research gaps. In response, the authors structure the BI scientific landscape and map its evolution to offer scholars a clear view of where research on BI stands and the way forward. For this endeavor, the authors systematically review articles published in top-tier ABS journals and identify 120 articles covering 35 years of scientific research on BI. The authors then run a co-citation analysis of selected articles and their reference lists. This yields the structuring of BI scholarly community around six research clusters: environmental scanning (ES), competitive intelligence (CI), market intelligence (MI), decision support (DS), analytical technologies (AT) and analytical capabilities (AC). The co-citation network exposed overlapping and divergent theoretical roots across the six clusters and permitted mapping the evolution of BI research following two pendulum swings. This study aims to contribute by structuring the theoretical landscape of BI research, deciphering the theoretical roots of BI literature, mapping the evolution of BI scholarly community and suggesting an agenda for future research.

Design/methodology/approach

This paper follows a systematic methodology to isolate peer-reviewed papers on BI published in top-tier ABS journals.

Findings

The authors present the structuring of BI scholarly community around six research clusters: ES, CI, MI, DS, AT and AC. The authors also expose overlapping and divergent theoretical roots across the six clusters and map the evolution of BI following two pendulum swings. In light of the structure and evolution of the BI research, the authors offer a future research agenda for BI research.

Originality/value

This study contributes by elucidating the theoretical underpinnings of the BI literature and shedding light upon the evolution, the contributions, and the research gaps for each of the six clusters composing the BI body of knowledge.